International Journal of Applied Electromagnetics and Mechanics

Consequent-pole permanent magnet synchronous machines (CP-PMSMs) have attracted considerable interest because they can save considerable permanent magnets and provide acceptable torque performance. The aim of this paper is to discover the full design space potential of the CP-PMSM by optimizing the topology of iron poles. ON/OFF method is used to manage the design region, finding the trade-off relationship between the average torque and torque ripple is the optimization targets. To this end, the topology of iron poles will be optimized by redistributing the iron and air material over the design region. A set of Delaunay mesh-based evolutionary operators is proposed and formulated to apply multi-objective evolutionary algorithms (MOEAs) to the formulated problem. The resulting topologies and convergence of two MOEAs are presented, and the optimization results are discussed.

The paper deals with an evolutionary method for solving many-objective optimization problems exhibiting a high-dimensionality objective space, which is a challenging problem. An application in the optimal synthesis of Compensation Networks (CNs) of wireless power transfer systems for charging the batteries of electric vehicles is developed. This design problem is characterized by a set of multiple objectives in mutual conflict, which should be simultaneously considered. The optimization aims to the maximization of both the efficiency and the transferred power; a further criterion selects the networks with a suitable profile of impedance vs. frequency. Moreover, the minimization of current and voltage values relevant to inductors and capacitors in the networks, respectively, is pursued. These five design criteria are optimized exploiting the concept of the degree of conflict, which is the core of the proposed method, named “EStra-many”. The method is applied by considering two approaches: the single-objective one, based on the degree of conflict function only, and the bi-objective approach in which the tradeoff between the degree of conflict function itself and another objective function (in turn, the efficiency, the transferred power, the distance of the resonance frequency from the supply frequency, the maximum value of the inductance current, the maximum value of the capacitor voltage, the distance from the Utopia point, and the number of inductors in the CN), is searched for. This way, all in one, seven different optimization problems are solved.

The main element of novelty of the paper is a method to solve an optimization problem characterized by a high number of objective functions. In view of this, instead of considering a weighted sum of the objectives, a preference function inspired by the concept of least-conflict solution is formulated accordingly, the preference function is minimized by a cost-effective evolutionary algorithm of lowest order.

The paper presents optimization of a newly developed hybrid electromagnetic system with magnetic flux modulation, for which patent application has been submitted. The efficiency of the system has been taken as objective function. The optimization factors include both geometrical and electrical parameters. Finite element method and three-dimensional coupled time-dependent electromagnetic field-electrical circuit analysis has been used for the solution of the forward problem. Response surface methodology and sequential linear programming are employed for the optimization.

Because reactors applied to electric vehicles are typically driven under a DC-biased current, a constant inductance property is required. When a high-performance DC-biased reactor is designed, the topology optimization (TO) is an effective design method owing to the high degree of structural change in the magnetic circuit. However, there exists no report regarding the TO method in the steady-state time domain with magnetic nonlinearity. In this paper, based on the time-domain adjoint variable method, a novel sensitivity analysis approach for the steady-state time domain with thorough consideration of the magnetic saturation is proposed. Through the proposed method, the TO of the iron core and winding structures in a pot-type reactor was carried out to enhance the DC-biased characteristics. Furthermore, the eddy current loss occurring on the aluminum plate installed outside the reactor was suppressed by the multi-objective TO. The interesting structure of a pot-type reactor that enhanced the DC-biased characteristics and reduced the eddy current loss on the outer conductor plate was also illustrated.

In this article we present an approach to the quantitative evaluation of the 3D printed sample made of polyethylene terephthalate glycol (PETG) using the active infrared thermography (AIT) method with halogen lamps excitation. For this purpose, numerical and experimental studies were carried out. The numerical model solved with finite element method (FEM) was used first to create a database of signals and further to train neural networks. The networks were trained to detect the heterogeneity of the internal structure of the tested printed sample and to estimate the defects position. After training, the performance of the network was validated with the data obtained in the experiment carried out with the active thermography regime on a real 3D print identical to the modelled one.

According to the article, locating moisture within the walls of buildings using electrical impedance tomography is discussed in detail. The algorithmic approach, whose role is to convert the input measurements into images, received excellent attention during the development process. Numerous models have been trained to generate tomographic images based on individual pixels in a given image based on machine learning methods. An array of categorisation data was then generated, which enabled the development of a classification model to solve the problem of optimal model selection for a given point on the screen. It was achieved in this manner by developing a pixel-oriented ensemble model (POE), the goal of which is to provide tomographic reconstructions of at least the same quality as homogeneous algorithmic approaches. Artificial neural networks (ANN), linear regression (LR), and the long short-term memory network (LSTM) were employed in the current research to get homogeneous machine learning results. An image reconstruction algorithm such as the ANN or the LR reconstructs the image pixel by pixel, which means that a different prediction model is trained for each image pixel. In the case of LSTM, a single network is responsible for creating the entire image. Then, using the POE algorithm, the best reconstruction method was fitted to each pixel of the output image while considering the measurement scenario provided to the program. As a result, each measurement consequences in a unique assignment of reconstructive procedures to individual pixels, which is different for each measurement. It is the capacity to maximise the selection of a prediction model while considering both a given pixel and a specific measurement vector that distinguishes the provided POE concept from other approaches.

In this paper CNNs are used for solving an optimization problem with two different approaches: CNN is used as a surrogate model of the forward problem, inserted in an optimization loop governed by a genetic algorithm, in the first approach, while a CNN is trained for solving directly the inverse problem in the second approach. The case study is the shape design of a magnetic core used for material testing.

This paper first presents an analytical model for calculating the shielding effectiveness of static magnetic fields using a rotating double-shell cylindrical shield with electromagnetically thin layers made of non-magnetic conducting material. Then a procedure is given for finding the optimum distance between the shells and their thickness for maximum mass reduction compared to a thick single shield for a given shielding factor.

The paper deals with the design by optimization in pre-sizing step of a drive system that combines a switched capacitor system and a permanent magnet synchronous machine (PMSM). The sizing of the converter and the machine are considered strongly dependent due to the series quasi-resonant topology. The system modelling is based on a semi-analytical approach validated by finite element (FE) simulations. It involves an analytical modelling of the electrical machine and a numerical time solving of the electrical circuit sub-model because of the natural switching events of diodes. The optimization is carried out by SQP (Sequential Quadratic Programming). The model gradients are computed combining mathematical laws, symbolic derivation and automatic differentiation. Finally, trade-offs and gains on the machine power density, the magnet volume and the efficiency are investigated through a Pareto approach.

The focus of this paper is an improved differentiability result for the forward map in inverse problems involving elliptic partial differential equations, and examination of its significance in the context of the electrical impedance tomography (EIT) problem with total variation (TV) regularization. We base our analysis on the Fréchet derivative of the mapping which takes a given conductivity function (spatially varying) in an electrostatic model to a corresponding elliptic PDE solution, and we develop the implications of a certain compactness property of the parameter space. By following this approach, we show Fréchet differentiability with a weaker norm (the L¹ norm) for the parameter space than is usually used (the L ^∞ norm), thus improving the Fréchet differentiability result. The EIT problem with TV regularization is well studied in the literature, and several authors have addressed the Fréchet differentiability question. However, to the best of our knowledge and as we argue, our result is the strongest analytical result in this context. Many derivative-based methods such as Gauss–Newton and Levenburg-Marquardt lie at the heart of many proposed methods for EIT, and the results described herein for these derivative calculations provide a firm theoretical footing for them.